Stimulus presentation can enhance spiking irregularity across subcortical and cortical regions

S, Fayaz; Fakharian, Mohammad Amin; Ghazizadeh, Ali

doi:10.1371/journal.pcbi.1010256

Cited by 13 publications

(23 citation statements)

References 78 publications

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“…Firing rate was significantly higher in larger display sizes (F 3,4752 = 5.49, p = 0.001), showing that neurons fired more when there were more objects on the screen despite the decreasing value signal (Fig 4A-B vs. Fig 3C-D). Firing rate variability can be parsed into two components one related to trial-to-trial firing rate variability (nRV) and the other related to within-trial spiking irregularity (nSI, (Fayaz, Fakharian, & Ghazizadeh, 2022)). Consistent with previous findings, nRV was reduced after display onset (Churchland et al, 2011; Fayaz et al, 2022).…”

Section: Resultsmentioning

confidence: 99%

Prefrontal Cortex Encodes Value Pop-out in Visual Search

Abbaszadeh

Panjehpour

Alemohammad

et al. 2023

Preprint

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Recent evidence shows that long-term object value association can lead to efficient visual search. However, the neural mechanism of this value pop-out has yet to be understood. Given the known role of the ventrolateral prefrontal cortex (vlPFC) in visual search and value memory, we recorded its single-unit activity (n=526) in two macaque monkeys while they engaged in the value-driven search. Monkeys had to determine whether a high-value target was present within a variable number of low-value objects. Interestingly, differential neural firing, as well as gamma-band power, indicated the presence of a target within ~150ms of display onset. This differential activity was negatively correlated with search time and became less display size-dependent for more efficient searches. On the other hand, neural firing and its variability were higher in inefficient searches. These findings reveal the neural code within vlPFC for rapid detection of valuable targets, which can be crucial for animals faced with competition.

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Section: Resultsmentioning

confidence: 99%

Prefrontal Cortex Encodes Value Pop-out in Visual Search

Abbaszadeh

Panjehpour

Alemohammad

et al. 2023

Preprint

Self Cite

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“…In cortical neural networks, an optimal system state in the present context must be selected from a large probability distribution. Neuroscientists have previously modeled this inherently probabilistic computation with Bayesian statistics [50], random-connection models [51], or fanofactor analysis of spike variance over time [54]. This report shows that non-deterministic signaling outcomes can be achieved through a mechanistic (not a statistically random) process.…”

Section: Discussionmentioning

confidence: 99%

Random electrical noise drives non-deterministic computation in cortical neural networks

Stoll

2022

Preprint

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In cortical neurons, spontaneous membrane potential fluctuations affect the likelihood of firing an action potential. Yet despite retaining sensitivity to random electrical noise in gating signaling outcomes, these cells achieve highly accurate computations with extraordinary energy efficiency. A new approach models the inherently probabilistic nature of cortical neuron firing as a thermodynamic process of non-deterministic computation. Typically, the cortical neuron is modeled as a binary computational unit, in either an off-state or an on-state, but here, the cortical neuron is modeled as a two-state quantum system, with some probability of switching from an off-state to an on-state. This approach explicitly takes into account the contribution of random electrical noise in gating signaling outcomes, particularly during cortical up-states. In this model, the membrane potential is described as the mixed sum of all component microstates, or the quantity of von Neumann entropy encoded by the computational unit. This distribution of macrostates is given by a density matrix, which undergoes a unitary change of basis as each unit, System A, interacts with its surrounding environment, System B. Any linear correlations reduce the number of distinguishable pure states, leading to the selection of an optimal system state in the present context. This process of information compression is shown to be equivalent to the extraction of predictive value from a thermodynamic quantity of information. Calculations demonstrate that estimated coulomb scattering profiles and decoherence timescales in cortical neurons are consistent with a quantum system, with random electrical noise driving signaling outcomes.

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“…This model of neural computation both vindicates and elaborates Friston's free energy principle [21][22][23][24], providing a thermodynamic basis for the reduction of 'surprise' during predictive processing. While prior efforts used statistical methods to model the inherently probabilistic patterns of cortical neural network activity [1][2][3]18], the present model usefully shows how energy-efficient nondeterministic computation might be achieved by following thermodynamic laws.…”

Section: Discussionmentioning

confidence: 99%

“…To compute the most likely state of the surrounding environment, a cortical neural network must select an optimal system state in the present context from a large probability distribution. Researchers have previously modeled this inherently probabilistic computation with Bayesian statistics [1], random-connection models [2], or fanofactor analysis of spike variance over time [3]. For individual neurons, the Hodgkin-Huxley equations provide a good approximation of firing patterns under steadystate conditions [4].…”

Section: Introductionmentioning

confidence: 99%

A thermodynamical model of non-deterministic computation in cortical neural networks

Stoll

2022

Preprint

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Neuronal populations in the cerebral cortex engage in probabilistic coding, effectively encoding the state of the surrounding environment with high accuracy and extraordinary energy efficiency. A new approach models the inherently probabilistic nature of cortical neuron signaling outcomes as a thermodynamic process of non-deterministic computation. A mean field approach is used, with the trial Hamiltonian maximizing free energy and minimizing the net quantity of temperature-entropy, compared with a reference Hamiltonian. Thermodynamic quantities are always conserved during the computation; free energy must be expended to produce information, and free energy is released during information compression, as correlations are identified between the encoding system and its surrounding environment. Due to the relationship between the Gibbs free energy equation and the Nernst equation, any increase in free energy is paired with a local decrease in membrane potential. As a result, this process of thermodynamic computation adjusts the likelihood of each neuron firing an action potential. This model shows that non-deterministic signaling outcomes can be achieved by noisy cortical neurons, through an energy-efficient computational process that involves optimally redistributing a Hamiltonian over some time evolution. Calculations demonstrate that the energy efficiency of the human brain is consistent with this model of non-deterministic computation, with net entropy production far too low to retain the assumptions of a classical system.

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Stimulus presentation can enhance spiking irregularity across subcortical and cortical regions

Cited by 13 publications

References 78 publications

Prefrontal Cortex Encodes Value Pop-out in Visual Search

Prefrontal Cortex Encodes Value Pop-out in Visual Search

Random electrical noise drives non-deterministic computation in cortical neural networks

A thermodynamical model of non-deterministic computation in cortical neural networks

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